(Non-)Parametric Recoverability of Preferences and Choice Prediction
通过两阶段自适应实验设计，比较风险决策中常用参数模型与非参数无差异曲线边界在预测选择上的表现，发现参数模型虽假设严格但预测损失很小。
Abstract Simple functional forms for utility require restrictive structural assumptions that are often contrary to observed behavior. Even so, they are widely used in applied economic research. I address this issue using a two-part adaptive experimental design to compare the predictions of a popular parametric model of decision making under risk to those of non-parametric bounds on indifference curves. Interpreting the latter as an approximate upper bound, I find the parametric model sacrifices very little in terms of predictive success. This suggests that, despite their restrictiveness, simple functional forms may nevertheless be useful representations of preferences over risky alternatives.